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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# softmax回归\n",
    "通过逻辑回归我们已经了解了模型的基本分类，逻辑回归是处理二分类问题，但是现实中有许多场景是需要多分类的，所以需要一种模型能够处理多分类，本文介绍的softmax回归模型就是能够处理多分类的，它的预测结果是一个向量，下面我们详细介绍模型的基本构成。\n",
    "\n",
    "## softmax回归模型\n",
    "+ 数据：根据问题来的，比如预测图片里面是汽车、猫、狗等，我们会收集很多图片，而彩色图片一般有3个通道，每个通道均是28*28的矩阵，每个像素点取值是0-256之间，从图片中我们可以提炼很多变量，当然直接将每个像素点也可以当成一个变量，还会收集图片真正包含的图片是什么【答案或者目标】，这个就是训练样本\n",
    "+ 模型：假设softmax回归模型的预测结果有3类\n",
    "  - O1 = b11 + a11\\*x1 + a12\\*x2 + a1n\\*xm\n",
    "  - O2 = b21 + a21\\*x1 + a22\\*x2 + a2n\\*xm\n",
    "  - O3 = b31 + a31\\*x1 + a32\\*x2 + a3n\\*xm \n",
    "\n",
    "  那么预测结果向量为：\\[y_hat1=exp(O1)/sum(exp(Oi)), y_hat2=exp(O2)/sum(exp(Oi)), y_hat3=exp(O3)/sum(exp(Oi))\\]\n",
    "+ 参数：b11 a11 a12 a1m 就是参数\n",
    "+ 损失函数：z = sum(-(y1\\*log(y_hat1) + y2\\*log(y_hat2) + y3\\*log(y_hat3)))/n，n 是样本数量\n",
    "+ 优化方法：梯度下降等，得益于 PyTorch 自动求梯度，我们不再需要显示求出损失函数的导数\n",
    "\n",
    "可以看到，逻辑回归的模型一部分和线性回归很相似，这也是它名字中包含回归的原因。\n",
    "\n",
    "本篇开始，因为我们已经掌握了模型基本构造原理，后续模型均是通过 PyTorch Lightning 高级 api 直接实现， https://github.com/PyTorchLightning/pytorch-lightning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 训练样本准备\n",
    "下面先了解一下后面我们经常需要用到的数据集，Fashion-MNIST，图像分类数据集中最常用的是手写数字识别数据集MNIST，但大部分模型在MNIST上的分类精度都超过了95%，也就是说MNIST数据集可以说是深度学习中的 HelloWorld，被玩坏了，那我们就玩点图像内容更加复杂的数据集 Fashion-MNIST（这个数据集也比较小，只有几十M，没有GPU的电脑也能吃得消）。    \n",
    "本节我们将使用 torchvision 包，它是服务于 PyTorch 深度学习框架的，主要用来构建计算机视觉模型。torchvision 主要由以下几部分构成：\n",
    "\n",
    "+ torchvision.datasets: 一些加载数据的函数及常用的数据集接口\n",
    "+ torchvision.models: 包含常用的模型结构（含预训练模型），例如AlexNet、VGG、ResNet等\n",
    "+ torchvision.transforms: 常用的图片变换，例如裁剪、旋转等\n",
    "+ torchvision.utils: 其他的一些有用的方法\n",
    "\n",
    "### 1.1 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "mnist_train = torchvision.datasets.FashionMNIST('../../datas', train=True, download=True, transform=transforms.ToTensor())\n",
    "mnist_test = torchvision.datasets.FashionMNIST('../../datas', train=False, download=True, transform=transforms.ToTensor())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'torchvision.datasets.mnist.FashionMNIST'>\ntrain sample len = 60000\ntest sample len = 10000\none picture shape = torch.Size([28, 28])\ntorch.Size([1, 28, 28]) 9\n"
    }
   ],
   "source": [
    "# 打印数据集的类型，以及训练样本和测试样本的数量\n",
    "print(type(mnist_train))\n",
    "print('train sample len = {}'.format(len(mnist_train)))\n",
    "print('test sample len = {}'.format(len(mnist_test)))\n",
    "print('one picture shape = {}'.format(mnist_train.train_data[0].shape))\n",
    "feature, label = mnist_train[0]\n",
    "print(feature.shape, label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 查看图片\n",
    "可以通过 matplotlib imshow 函数来绘制前10张图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# 获取每一个图片对应类别的英文解释\n",
    "def get_fashion_mnist_label_name(label_id):\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return text_labels[label_id]\n",
    "\n",
    "def show_fasion_mnist_head(head_num, mnist):\n",
    "    _, figs = plt.subplots(1, head_num, figsize=(12, 12))\n",
    "    for i in range(0, head_num):\n",
    "        imgs, label_id = mnist[i]\n",
    "        f = figs[i]\n",
    "        f.imshow(imgs.view(28, 28))\n",
    "        f.set_title(get_fashion_mnist_label_name(label_id))\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()\n",
    "show_fasion_mnist_head(10, mnist_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "tensor([[0.1028, 0.0969, 0.1025, 0.0980, 0.0991, 0.1017, 0.0989, 0.0977, 0.1016,\n         0.1008]], grad_fn=<SoftmaxBackward>)"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "l1 = torch.nn.Linear(28*28, 10)\n",
    "torch.softmax(l1(mnist_train[0][0].view(1, 28*28) / 256), dim=-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. softmax模型实现"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import nessasary lib\n",
    "import os\n",
    "import torch\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import TensorDataset\n",
    "import pytorch_lightning as pl\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "class SoftMaxModel(pl.LightningModule):\n",
    "\n",
    "    def __init__(self):\n",
    "        super(SoftMaxModel, self).__init__()\n",
    "        # 定义模型结构\n",
    "        self.l1 = torch.nn.Linear(28*28, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        # 必须：定义模型\n",
    "        return torch.log_softmax(self.l1(x.view(x.size(0), -1)), dim=1)\n",
    "\n",
    "    def training_step(self, batch, batch_nb):\n",
    "        # 必须提供：定于训练过程\n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        loss = F.nll_loss(y_hat, y)\n",
    "        tensorboard_logs = {'train_loss': loss}\n",
    "        return {'loss': loss, 'log': tensorboard_logs}\n",
    "\n",
    "    def test_step(self, batch, batch_nb):\n",
    "        # 可选提供：定义测试过程\n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        return {'test_loss': F.nll_loss(y_hat, y)}\n",
    "\n",
    "    def test_epoch_end(self, outputs):\n",
    "        # 可选提供：定义测试过程\n",
    "        avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()\n",
    "        logs = {'test_loss': avg_loss}\n",
    "        return {'test_loss': avg_loss, 'log': logs, 'progress_bar': logs}\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        # 必须提供：定义优化器\n",
    "        # can return multiple optimizers and learning_rate schedulers\n",
    "        # (LBFGS it is automatically supported, no need for closure function)\n",
    "        return torch.optim.SGD(self.parameters(), lr=0.01)\n",
    "\n",
    "    def train_dataloader(self):\n",
    "        # 必须提供：提供训练数据集\n",
    "        mnist_train = torchvision.datasets.FashionMNIST('../../datas', train=True, download=True,\n",
    "            transform=transforms.Compose([\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize((0.1307,), (0.3081,))\n",
    "            ]))\n",
    "        return DataLoader(mnist_train, batch_size=128, shuffle=True, num_workers=4)\n",
    "\n",
    "    def test_dataloader(self):\n",
    "        # 可选提供：提供测试数据集\n",
    "        mnist_test = torchvision.datasets.FashionMNIST('../../datas', train=False, download=True,\n",
    "            transform=transforms.Compose([\n",
    "                transforms.ToTensor(),\n",
    "                transforms.Normalize((0.1307,), (0.3081,))\n",
    "            ]))\n",
    "        return DataLoader(mnist_test, batch_size=128, shuffle=False, num_workers=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": "GPU available: False, used: False\nTPU available: False, using: 0 TPU cores\n\n  | Name | Type   | Params\n--------------------------------\n0 | l1   | Linear | 7 K   \nEpoch 20: 100%|██████████| 469/469 [00:03<00:00, 123.65it/s, loss=0.412, v_num=38]\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "1"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "softmax_model = SoftMaxModel()\n",
    "\n",
    "# most basic trainer, uses good defaults (1 gpu)\n",
    "trainer = pl.Trainer(max_epochs=20)\n",
    "trainer.fit(softmax_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Testing:  96%|█████████▌| 76/79 [00:01<00:00, 57.76it/s]--------------------------------------------------------------------------------\nTEST RESULTS\n{'test_loss': tensor(0.4524)}\n--------------------------------------------------------------------------------\nTesting: 100%|██████████| 79/79 [00:01<00:00, 56.52it/s]\n"
    }
   ],
   "source": [
    "res = trainer.test()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 查看预测结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# 获取每一个图片对应类别的英文解释\n",
    "mnist_test = torchvision.datasets.FashionMNIST('../../datas', train=False, download=True,\n",
    "    transform=transforms.Compose([\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize((0.1307,), (0.3081,))\n",
    "    ]))\n",
    "def get_fashion_mnist_label_name(label_id):\n",
    "    text_labels = ['t-shirt', 'trouser', 'pullover', 'dress', 'coat',\n",
    "                   'sandal', 'shirt', 'sneaker', 'bag', 'ankle boot']\n",
    "    return text_labels[label_id]\n",
    "\n",
    "def show_fasion_mnist_head(head_num, mnist):\n",
    "    _, figs = plt.subplots(1, head_num, figsize=(12, 12))\n",
    "    for i in range(0, head_num):\n",
    "        imgs, t_label_id = mnist[i]\n",
    "        p_label_id = torch.argmax(softmax_model(imgs))\n",
    "        f = figs[i]\n",
    "        f.imshow(imgs.view(28, 28))\n",
    "        f.set_title(get_fashion_mnist_label_name(t_label_id) + '\\n' + get_fashion_mnist_label_name(p_label_id))\n",
    "        f.axes.get_xaxis().set_visible(False)\n",
    "        f.axes.get_yaxis().set_visible(False)\n",
    "    plt.show()\n",
    "\n",
    "show_fasion_mnist_head(10, mnist_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 查看准确率和混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pytorch_lightning.metrics.functional import confusion_matrix\n",
    "from pytorch_lightning.metrics.functional import accuracy\n",
    "imgs, target = iter(softmax_model.test_dataloader()).next()\n",
    "p_labels_logic = softmax_model(imgs)\n",
    "p_labels = torch.argmax(p_labels_logic, dim=1)\n",
    "accuracy = accuracy(p_labels, target)\n",
    "cm = confusion_matrix(p_labels, target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "accuracy= tensor(0.8594)\nconfusion_matrix=\n tensor([[12.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n        [ 0., 13.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n        [ 1.,  0., 13.,  0.,  0.,  0.,  3.,  0.,  0.,  0.],\n        [ 1.,  0.,  0.,  9.,  1.,  0.,  0.,  0.,  0.,  0.],\n        [ 0.,  0.,  5.,  0.,  7.,  0.,  0.,  0.,  0.,  0.],\n        [ 0.,  0.,  0.,  0.,  0., 12.,  0.,  0.,  0.,  0.],\n        [ 1.,  0.,  0.,  0.,  2.,  0.,  7.,  0.,  0.,  0.],\n        [ 0.,  0.,  0.,  0.,  0.,  1.,  0., 14.,  0.,  0.],\n        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0., 16.,  0.],\n        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  3.,  0.,  7.]])\n"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 360x1440 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "print('accuracy=', accuracy)\n",
    "print('confusion_matrix=\\n', cm)\n",
    "cm_numpy = cm.numpy()\n",
    "class_nums = cm_numpy.sum(axis=0)\n",
    "plt.figure(figsize=(5, 20))\n",
    "plt.imshow(cm_numpy/class_nums, interpolation='nearest', cmap=plt.cm.Blues, vmin=0, vmax=1)\n",
    "plt.show()"
   ]
  }
 ]
}